Abstract
The utilization of Unmanned Aerial Vehicles (UAVs) like drones for indoor data gathering or sensing applications have gained popularity over the last decade. Indoor UAV navigation is a complex process, which involves several tasks such as mapping, localization, and path planning with obstacle avoidance. In this paper, a Particle Filter-based Indoor Navigation (PFIN) framework is proposed for the drone navigation process. In PFIN, Quadcopter Mapping Algorithm (QMA) is proposed using particle filter analysis to aid in mapping for indoor navigation. In addition, particle filter-based Optimized Localization Algorithm (OLA) and Adaptive Velocity Procedure (AVP) are proposed for the purpose of enhancing the precision in localization and to improve the velocity estimation for collision avoidance, respectively. Thus, the proposed PFIN framework is experimented using Software-In-The-Loop (SITL) tools such as Robot Operating System (ROS) and Gazebo for visualizing its behavior, and Crazyflie 2.0 drone assisted Hardware-In-The-Loop (HITL) simulation in verifying the correctness of the algorithms in a laboratory setup. The PFIN framework reduces position error on an average by 14% than the conventional Extended Kalman Filter (EKF) model. The SITL and HITL simulations demonstrate the efficiency of the algorithms through improved precision in UAV exploration.
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